一种面向查询的多文档摘要方法

面向查询的多文档摘要技术有两个难点 第一,为了保证摘要与查询密切相关,容易造成摘要内容重复,不够全面;第二,原始查询难以完整描述查询意图,需进行查询扩展,而现有查询扩展方法多依赖于外部语义资源。针对以上问题,该文提出一种面向查询的多文档摘要方法,利用主题分析技术识别出当前主题下的子主题,综合考虑句子所在的子主题与查询的相关度以及子主题的重要度两方面因素来选择摘要句,并根据词语在子主题之间的共现信息,在不使用任何外部知识的情况下,进行查询扩展。在DUC2006评测语料上的实验结果表明,与Baseline系统相比,该系统取得了更高的ROUGE评价值,基于子主题的查询扩展方法则进一步提高了摘要的质量。

An Approach to Query-focused Multi-Document Summarization

There are two difficulties in the technique of query-focused multi-document summarization. First, to ensure the high relevancy with the query, the summarization tends to be repetitive. Second, the original query needs to be expanded to fully reflect user’s intention, but current query expansion methods usually depend on exterior linguistic resources. To solve the above problems, this paper proposes a query-focused multi-document summarization approach, in which subtopics are identified by topic analysis technique. While selecting sentences, both the relevancy with query and the importance of the subtopic are considered. Then, the query is expanded according to the co-occurrence of words among subtopics without using any external knowledge. Experimental results on DUC2006 corpus show that the new approach achieves higher performance than the baseline system. The query expansion method further improved the summarization quality.
Key wordsquery-focused;multi-document summarization;subtopic;relevancy;query expansion